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This paper presents a technique for diagnosis of the type of fault and the faulty
phase on overhead transmission line. The proposed method is based on the multiresolution
STransform and Parseval’s theorem. STransform is used to produce instantaneous
frequency vectors of the voltage signals of the three phases, and then the energies of these
vectors, based on the Parseval’s theorem, are utilized as inputs to a Probabilistic Neural
Network (PNN). The power system network considered in this study is three phase
Transmission line with unbalanced loading simulated in the PowerSim Toolbox of
MATLAB. The fault conditions are simulated by the variation of fault location, fault
resistance, fault inception angle. The training is conducted by programming in MATLAB.
The robustness of the proposed scheme is investigated by synthetically polluting the
simulated voltage signals with White Gaussian Noise. The suggested method has produced
fast and accurate results. Estimation of fault location is intended to be conducted in future.
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